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• Investing in observability and reliability preemptively before experiencing issues
• Using infrastructure as code, specifically Terraform and Argo CD, to manage multi-cluster deployments and ensure consistency
• Creating custom metrics, such as Kubeflow state metrics, to track specific product needs and enable effective SLOs and alerts

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• ML Ops has different requirements than DevOps, such as dealing with large amounts of data and longer processing times
• Flight is a platform that can help with ML Ops workflows, providing features such as composition, caching, and validation
• Flight can handle individual containers for task execution environments, but not yet streaming data
• Flight has been well-received by data scientists for its ease of use and out-of-the-box parallelism

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• Backstage was developed by Spotify to address the problem of scattered engineering tools and lack of information about services.
• It started as System C, an inventory of services, but was reworked to make it more extensible and accommodate other categories of entities like data pipelines and websites.
• Backstage has a plug-in framework that allows for more isolated feature development and a unified interface for all tooling, making it easier for engineers to venture into other domains.
• The use of Backstage has resulted in a significant reduction in onboarding time for new engineers at Spotify.

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• Spotify is building an encoder system that can encode a user's state into embeddings that are sensitive to actions and changes in satisfaction.
• They have built a simulator that can simulate user reactions to certain content, which is used to train the recommendation algorithm.
• A/B testing is used to compare agents trained on good simulators versus less good simulators.
• Spotify is transitioning to a more farsighted approach to content recommendation, optimizing for long-term fulfilling content diet rather than clicks or streams.
• They are investing in data instrumentation to understand how users interact with their content and to create reusable data sets.
• Spotify has shared machine learning models that provide information on user affinities, similarities, and clustering, which are useful for many different features.
• Machine learning models are created for specific use cases, such as Discover Weekly or search, and optimize for different goals.